Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BTβSMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive β-hill climbing, AβHC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BTβSMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BTβSMA-1 and BTβSMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BTβSMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BTβSMA algorithm in terms of solution precision.
This study used a method based on convolutional neural network model, VGG16, to identify images of weeds in the field. As the basic network, VGG16 has very good classification performance, and the network structure is unconventional. It is relatively easy to modify. It can fine-tune other data sets on this basis. Therefore, the transfer learning method is applied to our own Kaggle competition website. Download the weed data set. The site covers approximately 3, 500 images in 12 categories. Due to limited data and computational power, our model fixes the first 14 layers of VGG16 parameters for layer-by-layer automatic extraction of features, adding an average pooling layer, convolution layer, Dropout layer, fully connected layer, and softmax for classifiers. The layer has a total of 5 layers, for a total of 19 layers. The experimental results show that the final model performs well in the classification effect of 12 weed images. The accuracy rate on the training set is 98.99%, and the accuracy on the verification set is 91.08%. It can be applied to crop weed identification. It provides accurate and reliable judgment basis for positioning and quantitative chemical pesticide spraying, and is the key to achieving refined agriculture.
Barnacles mating optimizer (BMO) is an evolutionary algorithm that simulates the mating and reproductive behavior of barnacle population. In this paper, an improved Barnacles mating optimizer based on logistic model and chaotic map (LCBMO) was proposed to produce the high-quality optimal result. Firstly, the logistic model is introduced into the native BMO to realize the automatic conversion parameters. This strategy maintains a proper relationship between exploitation and exploration. Then, the chaotic map is integrated to enhance the exploitation capability of the algorithm. After that, six variants based on LCBMO are compared to find the best algorithm on benchmark functions. Moreover, to the knowledge of the authors, there is no previous study on this algorithm for multilevel color image segmentation. LCBMO takes Masi entropy as the objective function to find the optimal threshold. By comparing different thresholds, different types of images, different optimization algorithms, and different objective functions, our proposed technique is reliable and promising in solving color image multilevel thresholding segmentation. Wilcoxon rank-sum test and Friedman test also prove that the simulation results are statistically significant. INDEX TERMS Barnacles mating optimizer, logistic model, chaotic map, masi entropy, multilevel thresholding, color image segmentation.
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